A dynamic insertion approach for multi-dimensional data using index structures

Nowadays large volumes of data with high dimensionality are being generated in many fields. Most existing indexing techniques degrade rapidly when dimensionality goes higher. A large amount of data sets are time related, and the existence of the obsolete data in the data sets may seriously degrade the data processing. In our previous work[7], we proposed ClusterTree⁺, a new indexing approach representing clusters generated by any existing clustering approach. It is a hierarchy of clusters and subclusters which incorporates the cluster representation into the index structure to achieve effective and efficient retrieval. It also has features from the time perspective. Each new data item is added to the ClusterTree⁺ with the time information which can be used later in the data update process for the acquisition of the new cluster structure. To improve the performance of this index structure, we propose a dynamic insertion approach for time-related multi-dimensional data based on a modified ClusterTree⁺, keeping the index structure always in the most updated status which can further promote the efficiency and effectiveness of data query, data update, etc. This approach is highly adaptive to any kind of clusters.